python_code stringlengths 0 869k |
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import os
import uuid
from typing import Dict, List, Optional
from grpc._channel import _InactiveRpcError
from qdrant_client.http.exceptions import UnexpectedResponse
from qdrant_client.http.models import PayloadSchemaType
from datastore.datastore import DataStore
from models.models import (
DocumentChunk,
Do... |
import os
from typing import Any, List
from datetime import datetime
from supabase import Client
from datastore.providers.pgvector_datastore import PGClient, PgVectorDataStore
from models.models import (
DocumentMetadataFilter,
)
SUPABASE_URL = os.environ.get("SUPABASE_URL")
assert SUPABASE_URL is not None, "SUP... |
import asyncio
import os
import re
import time
import base64
from typing import Dict, List, Optional, Union
from datastore.datastore import DataStore
from models.models import DocumentChunk, DocumentChunkMetadata, DocumentChunkWithScore, DocumentMetadataFilter, Query, QueryResult, QueryWithEmbedding
from loguru import ... |
import os
from typing import Dict, List, Any, Optional
import elasticsearch
from elasticsearch import Elasticsearch, helpers
from loguru import logger
from datastore.datastore import DataStore
from models.models import (
DocumentChunk,
DocumentChunkWithScore,
DocumentMetadataFilter,
QueryResult,
Q... |
import os
from typing import Any, Dict, List, Optional
import pinecone
from tenacity import retry, wait_random_exponential, stop_after_attempt
import asyncio
from loguru import logger
from datastore.datastore import DataStore
from models.models import (
DocumentChunk,
DocumentChunkMetadata,
DocumentChunkWi... |
import os
from typing import Any, List
from datetime import datetime
import numpy as np
from psycopg2 import connect
from psycopg2.extras import DictCursor
from pgvector.psycopg2 import register_vector
from services.date import to_unix_timestamp
from datastore.providers.pgvector_datastore import PGClient, PgVectorDat... |
import json
import os
import asyncio
from loguru import logger
from typing import Dict, List, Optional
from pymilvus import (
Collection,
connections,
utility,
FieldSchema,
DataType,
CollectionSchema,
MilvusException,
)
from uuid import uuid4
from services.date import to_unix_timestamp
fr... |
# This is a version of the main.py file found in ../../server/main.py that also gives ChatGPT access to the upsert endpoint
# (allowing it to save information from the chat back to the vector) database.
# Copy and paste this into the main file at ../../server/main.py if you choose to give the model access to the upsert... |
# This is a version of the main.py file found in ../../../server/main.py without authentication.
# Copy and paste this into the main file at ../../../server/main.py if you choose to use no authentication for your retrieval plugin.
from typing import Optional
import uvicorn
from fastapi import FastAPI, File, Form, HTTPE... |
import uuid
import json
import argparse
import asyncio
from loguru import logger
from models.models import Document, DocumentMetadata
from datastore.datastore import DataStore
from datastore.factory import get_datastore
from services.extract_metadata import extract_metadata_from_document
from services.pii_detection im... |
import uuid
import json
import argparse
import asyncio
from loguru import logger
from models.models import Document, DocumentMetadata
from datastore.datastore import DataStore
from datastore.factory import get_datastore
from services.extract_metadata import extract_metadata_from_document
from services.pii_detection im... |
import uuid
import zipfile
import os
import json
import argparse
import asyncio
from loguru import logger
from models.models import Document, DocumentMetadata, Source
from datastore.datastore import DataStore
from datastore.factory import get_datastore
from services.extract_metadata import extract_metadata_from_docume... |
from models.models import Source
from services.openai import get_chat_completion
import json
from typing import Dict
import os
from loguru import logger
def extract_metadata_from_document(text: str) -> Dict[str, str]:
sources = Source.__members__.keys()
sources_string = ", ".join(sources)
# This prompt is ... |
import os
from services.openai import get_chat_completion
def screen_text_for_pii(text: str) -> bool:
# This prompt is just an example, change it to fit your use case
messages = [
{
"role": "system",
"content": f"""
You can only respond with the word "True" or "Fals... |
import os
from io import BufferedReader
from typing import Optional
from fastapi import UploadFile
import mimetypes
from PyPDF2 import PdfReader
import docx2txt
import csv
import pptx
from loguru import logger
from models.models import Document, DocumentMetadata
async def get_document_from_file(
file: UploadFile... |
from typing import List
import openai
import os
from loguru import logger
from tenacity import retry, wait_random_exponential, stop_after_attempt
@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(3))
def get_embeddings(texts: List[str]) -> List[List[float]]:
"""
Embed texts using Op... |
import arrow
from loguru import logger
def to_unix_timestamp(date_str: str) -> int:
"""
Convert a date string to a unix timestamp (seconds since epoch).
Args:
date_str: The date string to convert.
Returns:
The unix timestamp corresponding to the date string.
If the date string c... |
from typing import Dict, List, Optional, Tuple
import uuid
import os
from models.models import Document, DocumentChunk, DocumentChunkMetadata
import tiktoken
from services.openai import get_embeddings
# Global variables
tokenizer = tiktoken.get_encoding(
"cl100k_base"
) # The encoding scheme to use for tokeniza... |
from setuptools import setup, find_packages
setup(
name="neuron_explainer",
packages=find_packages(),
version="0.0.1",
author="OpenAI",
install_requires=[
"httpx>=0.22",
"scikit-learn",
"boostedblob>=0.13.0",
"tiktoken",
"blobfile",
"numpy",
"... |
def standardize_azure_url(url):
"""Make sure url is converted to url format, not an azure path"""
if url.startswith("az://openaipublic/"):
url = url.replace("az://openaipublic/", "https://openaipublic.blob.core.windows.net/")
return url
|
import asyncio
import contextlib
import os
import random
import traceback
from asyncio import Semaphore
from functools import wraps
from typing import Any, Callable, Optional
import httpx
import orjson
def is_api_error(err: Exception) -> bool:
if isinstance(err, httpx.HTTPStatusError):
response = err.res... |
"""Utilities for formatting activation records into prompts."""
import math
from typing import Optional, Sequence
from neuron_explainer.activations.activations import ActivationRecord
UNKNOWN_ACTIVATION_STRING = "unknown"
def relu(x: float) -> float:
return max(0.0, x)
def calculate_max_activation(activation... |
# Dataclasses and enums for storing neuron-indexed information about activations. Also, related
# helper functions.
import math
from dataclasses import dataclass, field
from typing import List, Optional, Union
import urllib.request
import blobfile as bf
import boostedblob as bbb
from neuron_explainer.fast_dataclasses... |
from dataclasses import dataclass
from typing import List, Union
import blobfile as bf
from neuron_explainer.fast_dataclasses import FastDataclass, loads, register_dataclass
from neuron_explainer.azure import standardize_azure_url
import urllib.request
@register_dataclass
@dataclass
class TokensAndWeights(FastDatacl... |
from neuron_explainer.explanations.few_shot_examples import FewShotExampleSet
from neuron_explainer.explanations.prompt_builder import HarmonyMessage, PromptFormat, Role
from neuron_explainer.explanations.simulator import (
ExplanationNeuronSimulator,
ExplanationTokenByTokenSimulator,
)
def test_make_explanat... |
import json
import os
from dataclasses import dataclass
from neuron_explainer.activations.activations import ActivationRecord
@dataclass(frozen=True)
class Puzzle:
"""A puzzle is a ground truth explanation, a collection of sentences (stored as ActivationRecords) with activations
according to that explanation... |
from __future__ import annotations
from enum import Enum
from typing import TypedDict, Union
import tiktoken
HarmonyMessage = TypedDict(
"HarmonyMessage",
{
"role": str,
"content": str,
},
)
class PromptFormat(str, Enum):
"""
Different ways of formatting the components of a prom... |
"""Uses API calls to generate explanations of neuron behavior."""
from __future__ import annotations
import logging
import re
from abc import ABC, abstractmethod
from enum import Enum
from typing import Any, Optional, Sequence, Union
from neuron_explainer.activations.activation_records import (
calculate_max_act... |
# Dataclasses and enums for storing neuron explanations, their scores, and related data. Also,
# related helper functions.
from __future__ import annotations
import json
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import blobfile as bf
import boostedblob as bbb
fr... |
from __future__ import annotations
import asyncio
import logging
from typing import Any, Callable, Coroutine, Sequence
import numpy as np
from neuron_explainer.activations.activations import ActivationRecord
from neuron_explainer.explanations.calibrated_simulator import (
CalibratedNeuronSimulator,
LinearCali... |
from dataclasses import dataclass
from enum import Enum
from typing import List
from neuron_explainer.fast_dataclasses import FastDataclass
@dataclass
class Example(FastDataclass):
"""
An example list of tokens as strings corresponding to top token space inputs of a neuron, with a
string explanation of t... |
"""
Code for calibrating simulations of neuron behavior. Calibration refers to a process of mapping from
a space of predicted activation values (e.g. [0, 10]) to the real activation distribution for a
neuron.
See http://go/neuron_explanation_methodology for description of calibration step. Necessary for
simulating neu... |
import asyncio
from typing import Any
from neuron_explainer.explanations.explainer import (
TokenActivationPairExplainer,
TokenSpaceRepresentationExplainer,
)
from neuron_explainer.explanations.few_shot_examples import TEST_EXAMPLES, FewShotExampleSet
from neuron_explainer.explanations.prompt_builder import Ha... |
# Few-shot examples for generating and simulating neuron explanations.
from __future__ import annotations
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional
from neuron_explainer.activations.activations import ActivationRecord
from neuron_explainer.fast_dataclasses import FastD... |
"""Uses API calls to simulate neuron activations based on an explanation."""
from __future__ import annotations
import asyncio
import logging
from abc import ABC, abstractmethod
from collections import OrderedDict
from enum import Enum
from typing import Any, Optional, Sequence, Union
import numpy as np
from neuron_... |
# Utilities for dataclasses that are very fast to serialize and deserialize, with limited data
# validation. Fields must not be tuples, since they get serialized and then deserialized as lists.
#
# The unit tests for this library show how to use it.
import json
from dataclasses import dataclass, field, fields, is_data... |
from .fast_dataclasses import FastDataclass, dumps, loads, register_dataclass
__all__ = ["FastDataclass", "dumps", "loads", "register_dataclass"]
|
from dataclasses import dataclass
import pytest
from .fast_dataclasses import FastDataclass, dumps, loads, register_dataclass
# Inheritance is a bit tricky with our setup. dataclass_name must be set for instances of these
# classes to serialize and deserialize correctly, but if it's given a default value, then subc... |
# %%
import logging
from flask import Flask, request
from flask_cors import CORS
import json
import urllib.request
def load_az_json(url):
with urllib.request.urlopen(url) as f:
return json.load(f)
def start(
dev: bool = False,
host_name: str = "0.0.0.0",
port: int = 80,
):
app = Flask("... |
import multiprocessing
import os
import sys
import subprocess
from distutils import sysconfig
from distutils.command.build import build as DistutilsBuild
from setuptools import setup
def build_common(dynamic_library_extension, cmake_arg_list=None):
# On OSX CMake's FindPythonLibs is flaky; we need to supply lib a... |
from doom_py.vizdoom import *
import os
class Loader():
"""
This class converts file name to full paths to be imported
by the DoomGame
"""
def get_vizdoom_path(self):
package_directory = os.path.dirname(os.path.abspath(__file__))
return os.path.join(package_directory, 'bin/vizdoom')... |
#!/usr/bin/python
#####################################################################
# This script presents how to run some scenarios.
# Configuration is loaded from "../../examples/config/<SCENARIO_NAME>.cfg" file.
# <episodes> number of episodes are played.
# Random combination of buttons is chosen for every act... |
#!/usr/bin/python
#####################################################################
# This script presents how to make use of game variables to implement
# shaping using health_guided.wad scenario
# Health_guided scenario is just like health_gathering
# (see "../../scenarios/README.md") but for each collected med... |
#!/usr/bin/python
from __future__ import print_function
from vizdoom import *
from random import choice
from time import sleep
from time import time
game = DoomGame()
game.set_vizdoom_path("../../bin/vizdoom")
game.set_doom_game_path("../../scenarios/freedoom2.wad")
#game.set_doom_game_path("../../scenarios/doom2... |
#!/usr/bin/python
#####################################################################
# This script presents SPECTATOR mode. In SPECTATOR mode you play and
# your agent can learn from it.
# Configuration is loaded from "../../examples/config/<SCENARIO_NAME>.cfg" file.
#
# To see the scenario description go to "../.... |
#!/usr/bin/python
#####################################################################
# This script tests performance in frames per second.
# Change iters, resolution, window visibility, use get_ state or not.
# It should give you some idea how fast the framework can work on
# your hardware. The test involes copying... |
#!/usr/bin/python
#####################################################################
# This script presents different formats of the screen buffer.
# OpenCV is used here to display images, install it or remove any
# references to cv2
# Configuration is loaded from "../../examples/config/basic.cfg" file.
# <episodes>... |
#!/usr/bin/python
#####################################################################
# This script presents how to run deterministic episodes by setting
# seed. After setting the seed every episode will look the same (if
# agent will behave deterministicly of course).
# Configuration is loaded from "../../examples... |
#!/usr/bin/python
#####################################################################
# This script presents how to use the most basic features of the environment.
# It configures the engine, and makes the agent perform random actions.
# It also gets current state and reward earned with the action.
# <episodes> numbe... |
#!/usr/bin/python
from __future__ import print_function
from vizdoom import *
from random import choice
game = DoomGame()
game.set_vizdoom_path("../../bin/vizdoom")
# Use CIG example config or Your own.
game.load_config("../../examples/config/cig.cfg")
# Select game and map You want to use.
game.set_doom_game_path... |
#!/usr/bin/python
from __future__ import print_function
from vizdoom import *
from random import choice
game = DoomGame()
# Use CIG example config or Your own.
game.load_config("../../examples/config/cig.cfg")
# Select game and map You want to use.
game.set_doom_game_path("../../scenarios/freedoom2.wad")
#game.set_... |
#!/usr/bin/python
import itertools as it
import pickle
from random import sample, randint, random
from time import time
from vizdoom import *
import cv2
import numpy as np
import theano
from lasagne.init import GlorotUniform, Constant
from lasagne.layers import Conv2DLayer, InputLayer, DenseLayer, MaxPool2DLayer, get... |
#!/usr/bin/python
from __future__ import print_function
from vizdoom import *
from random import choice
game = DoomGame()
# Use CIG example config or Your own.
game.load_config("../../examples/config/cig.cfg")
# Select game and map You want to use.
game.set_doom_game_path("../../scenarios/freedoom2.wad")
#game.set_... |
import os
import pkg_resources
from setuptools import setup, find_packages
setup(
name="clip",
py_modules=["clip"],
version="1.0",
description="",
author="OpenAI",
packages=find_packages(exclude=["tests*"]),
install_requires=[
str(r)
for r in pkg_resources.parse_requirement... |
from clip.clip import tokenize as _tokenize, load as _load, available_models as _available_models
import re
import string
dependencies = ["torch", "torchvision", "ftfy", "regex", "tqdm"]
# For compatibility (cannot include special characters in function name)
model_functions = { model: re.sub(f'[{string.punctuation}]... |
from .clip import *
|
from collections import OrderedDict
from typing import Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1):
super().__init__()
# all conv layers have strid... |
import hashlib
import os
import urllib
import warnings
from typing import Any, Union, List
from pkg_resources import packaging
import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
from .model import build_model
from .simple_tokeni... |
import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
@lru_cache()
def default_bpe():
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corr... |
import numpy as np
import pytest
import torch
from PIL import Image
import clip
@pytest.mark.parametrize('model_name', clip.available_models())
def test_consistency(model_name):
device = "cpu"
jit_model, transform = clip.load(model_name, device=device, jit=True)
py_model, _ = clip.load(model_name, device... |
from setuptools import setup, find_packages
setup(
name="jcm",
version="0.1",
packages=find_packages(),
package_dir={"jcm": "jcm"},
install_requires=[
"wandb",
"clean-fid",
"torchvision",
"torch",
"tensorflow",
"tensorboard",
"absl-py",
... |
# Code modified from https://github.com/GaParmar/clean-fid/blob/main/cleanfid/fid.py
# Original license below:
# MIT License
#
# Copyright (c) 2021 Gaurav Parmar
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and as... |
# Copyright 2023 (c) OpenAI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
# Copyright 2023 (c) OpenAI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
# Copyright 2021 The Flax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
# Copyright 2023 (c) OpenAI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
# Copyright 2023 (c) OpenAI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
# Copyright 2023 (c) OpenAI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
# Copyright 2023 (c) OpenAI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
# Copyright 2023 (c) OpenAI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
# Copyright 2023 (c) OpenAI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
# Copyright 2023 (c) OpenAI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
# Copyright 2023 (c) OpenAI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
# Code adapted from https://github.com/google-research/google-research/tree/master/flax_models/cifar
# Original copyright statement:
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You ... |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
# Code from https://github.com/pcuenca/lpips-j/blob/main/src/lpips_j/lpips.py
#
# Original copyright statement:
# Copyright 2021 The DALL·E mini Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the L... |
from configs.default_cifar10_configs import get_default_configs
from configs.cifar10_k_ve import get_config as get_ref_config
import math
def get_config():
config = get_default_configs()
# training
training = config.training
training.sde = "kvesde"
training.loss = "consistency_adaptive"
train... |
from configs.default_cifar10_configs import get_default_configs
import math
def get_config():
config = get_default_configs()
# training
training = config.training
training.sde = "kvesde"
training.loss = "dsm"
training.batch_size = 512
training.n_iters = 400001
training.n_jitted_steps =... |
from configs.default_cifar10_configs import get_default_configs
from configs.cifar10_k_ve import get_config as get_ref_config
import math
def get_config():
config = get_default_configs()
# training
training = config.training
training.sde = "kvesde"
training.loss = "consistency_ema"
training.r... |
from configs.default_cifar10_configs import get_default_configs
from configs.cifar10_k_ve import get_config as get_ref_config
import math
def get_config():
config = get_default_configs()
# training
training = config.training
training.sde = "kvesde"
training.loss = "continuous"
training.ref_mo... |
from configs.default_cifar10_configs import get_default_configs
from configs.cifar10_k_ve import get_config as get_ref_config
import math
def get_config():
config = get_default_configs()
# training
training = config.training
training.sde = "kvesde"
training.loss = "progressive_distillation"
t... |
from configs.default_cifar10_configs import get_default_configs
import math
def get_config():
config = get_default_configs()
# training
training = config.training
training.sde = "kvesde"
training.loss = "dsm"
training.batch_size = 512
training.n_iters = 400001
training.n_jitted_steps =... |
from configs.default_cifar10_configs import get_default_configs
from configs.cifar10_k_ve import get_config as get_ref_config
import math
def get_config():
config = get_default_configs()
# training
training = config.training
training.sde = "kvesde"
training.loss = "consistency"
training.ref_m... |
import ml_collections
def get_default_configs():
config = ml_collections.ConfigDict()
# training
config.training = training = ml_collections.ConfigDict()
training.batch_size = 128
training.n_iters = 1300001
training.snapshot_freq = 50000
training.log_freq = 50
training.eval_freq = 100
... |
from tqdm import tqdm
from model import CLIPImage, CLIPText
import tensorflow as tf
import os
import numpy as np
from lucid.optvis import objectives, param
import lucid.optvis.render as render
from lucid.optvis.objectives import wrap_objective, diversity
import lucid.optvis.transform as transform
from lucid.misc.io im... |
from tokenizer import SimpleTokenizer
from model import CLIPImage, CLIPText
import tensorflow as tf
from lucid.misc.io import load
import numpy as np
def imresize(img, size, scale=255):
from PIL import Image
im = Image.fromarray((img*scale).astype(np.uint8) )
return np.array(im.resize(size, Image.BICUBIC))... |
from lucid.modelzoo.vision_base import Model
from lucid.optvis import render
import tensorflow as tf
from lucid.misc.io import load, save
class CLIPImage(Model):
image_value_range = (0, 255)
input_name = 'input_image'
def __init__(self):
self.model_name = "RN50_4x"
self.image_shape = [288,... |
# By Alec Radford
import html
import ftfy
import json
import regex as re
from functools import lru_cache
import tensorflow as tf
import blobfile
def pad(x, pad_length = 76):
z = np.zeros((pad_length))
z[0:len(x)] = x
return z
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and... |
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